Artificial intelligence (AI) is rapidly transforming the legal, regulatory and commercial landscape, driving fundamental changes in how organisations develop, deploy and contract for technology-enabled services. State laws in the United States remain fragmented and continue to evolve. At the same time, divergent global approaches are emerging, with the European Union (EU) adopting a comprehensive, risk-based regulatory regime and United States’ federal authorities pursuing a combination of regulatory guidance, judicial action and policy initiatives. This dynamic and multi-layered regulatory environment has created growing complexity and uncertainty for companies operating across jurisdictions and industries.

Against this backdrop, AI is reshaping traditional legal frameworks, challenging long-standing assumptions, and requiring a reimagination of well-established contractual norms. At the same time, the rapid growth of AI-driven businesses – particularly in innovation hubs such as Boston – has fuelled significant venture capital activity, reflecting the increasing strategic importance of AI across every sector of the economy. As AI adoption continues to accelerate, organisations must actively adapt both their legal strategies and commercial practices to address emerging risks, allocate responsibility effectively, and maintain a competitive advantage in an increasingly AI-driven world.

Regulatory Environment

Massachusetts

While Massachusetts has not yet enacted any specific statute comprehensively regulating the development or use of AI, Attorney General Andrea Joy Campbell issued an advisory in April 2024 clarifying that the Commonwealth’s consumer protection, anti-discrimination and data privacy laws apply to AI developers, suppliers and users just as they would in any other context. The advisory identified a non-exhaustive list of practices that may constitute unfair or deceptive acts under the Massachusetts Consumer Protection Act, including:

• falsely advertising the quality or usability of AI systems;
• supplying AI technology that is defective or impractical for the advertised purpose;
• misrepresenting an AI system’s reliability, safety, condition or manner of performance;
• offering for sale AI systems that are not fit for the ordinary purpose for which such systems are used;
• deploying deepfakes or voice cloning to perpetrate fraud; and
• using AI systems that produce discriminatory results in violation of the state’s civil rights laws.

It also clarified that Massachusetts’data security and incident response rules generally apply to AI systems that touch personal information, imposing prescriptive, technology-neutral security obligations on any person or entity that owns or licenses personal information about a Commonwealth resident. Similarly, Massachusetts’ anti-discrimination laws prohibiting discrimination on the basis of protected characteristics apply to developers, suppliers and users of AI systems, including technologies for hiring, tracking, evaluating or terminating employees.

Massachusetts has also adopted certain sector-specific rules related to automated recognition technologies, and the legislature is currently considering new legislation explicitly focused on AI issues. The proposals under consideration include:

• AI disclosure requirements;
• restrictions on AI-based employee monitoring;
• a bill targeting algorithmic discrimination that would impose strict liability and a private right of action;
• a bill establishing comprehensive data controller obligations regarding AI-related data processing; and
• legislation addressing the use of AI in healthcare settings, including a prohibition on AI making independent therapeutic decisions in behavioural health.

State regulation generally

Looking beyond Massachusetts, more than half of US state legislatures have enacted statutes that meaningfully regulate some aspect of AI. In general, these laws have aimed to:

• establish AI governance bodies (task forces, commissions and executive offices to guide AI policy and oversee government use of AI systems);
• mandate transparency (disclosing AI’s role in decision-making);
• prevent bias (algorithmic discrimination by AI systems in high-stakes decisions); and
• limit or prohibit “deepfakes” (realistic, artificial images, videos and audio generated by AI and used for malicious or deceptive purposes such as non-consensual pornography and election disinformation).

Federal regulation

At the federal level, the Trump administration released an AI action plan last July that emphasised federal deregulation, the acceleration of AI infrastructure, the promotion of open models, the securing of US hegemony and the removal of what the plan characterised as ideological biases. The plan followed President Trump’s January revocation of the Biden administration’s prior Executive Orders and priorities regarding AI. Among other recommendations, the plan called for:

• the identification and removal of federal regulations that hinder the development of AI technologies;
• the revision of environmental standards, permitting processes and procurement priorities to accelerate infrastructure development;
• the use of discretionary federal spending to challenge and dissuade state regulatory requirements;
• promoting access to large-scale computing resources and private-sector adoption of open source models; and
• directing the Department of Commerce to facilitate the exportation of US-developed “full-stack AI packages” and the global adoption of AI technologies that meet US technical and security standards.

In March 2026, the administration released an AI legislative framework further emphasising the goals above, calling on Congress to implement the administration’s policy objectives, and introducing additional priorities related to children, fair use, free speech and workforce education.

EU AI Act

By contrast, Europe has gone in a different direction, adopting comprehensive regulations in 2024, with phased implementation during 2025 and 2026. The EU AI Act contains five core principles, requiring uses of AI to be safe, transparent, traceable, non-discriminatory and environmentally friendly. Applications of AI technology are classified as posing unacceptable risk, high risk or limited risk. Deployment of AI technology classified as presenting unacceptable risk is prohibited, while high- and limited-risk AI uses are regulated, albeit at different levels. High-risk technologies, for example, require mandatory assessments of their potential impact on fundamental rights.

Limited-risk systems trigger less stringent obligations, including informing users when they are viewing AI-generated content. In all cases, AI tools must be overseen by humans, rather than by technology. European regulators also required measures to protect copyrights, certain mandatory disclosures and detailed technical documentation. Penalties for violations can be severe, and the Act applies to entities providing or deploying AI systems that are either used in the EU or that produce effects in the EU, wherever the entity may be based.

IP Strategy

In 2025, Boston was ranked as a state with one of the highest AI ecosystems throughout the continental United States (see here). As companies across every sector accelerate their adoption of AI tools, long-standing assumptions about patentability and inventorship are being tested. This shift is prompting organisations to rethink traditional intellectual property (IP) strategies and adapt them to a world where human and machine contributions increasingly intertwine.

A foundational principle of US patent law is that only a natural person can be named as an inventor. The US Patent and Trademark Office (USPTO)’s recent Inventorship Guidance for AI‑Assisted Inventions reaffirms this requirement, even as AI systems play a growing role in generating inventive concepts. US copyright law follows a similar rule: human authorship is required.

For patent applicants, inventions that incorporate AI-generated inputs or outputs raise new questions about obviousness, enablement and disclosure at the USPTO. Inventors must now determine how much detail to provide about their AI models, such as training data and model parameters, to demonstrate human conception and reduction to practice. The line between human insight and machine-generated output is becoming more complex to document.

AI systems also rely heavily on large, often sensitive datasets, including genomic sequences, compound libraries, biomarker repositories and user-generated information. As AI models are increasingly trained on combinations of proprietary, licensed and open‑source data, companies must confront difficult questions about data ownership, rights to AI-derived insights, public disclosure of sensitive information into open source models, and how to protect valuable datasets themselves. These issues are especially pressing in life sciences, where data assets can be as valuable as the inventions they enable.

The rapid integration of AI into research and development is creating a heightened need for cross-functional collaboration. Legal, scientific and technical teams must work together to document human contributions to AI‑assisted inventions, negotiate licensing agreements that address data and model usage, and ensure compliance with evolving global standards.

While AI is accelerating innovation at an unprecedented pace, it is also destabilising traditional IP structures. Companies that wish to safeguard their competitive advantage must proactively adapt their IP strategies to the realities of AI‑enabled discovery and development.

Sourcing and Technology Transactions

The exponential proliferation of AI and deep machine learning across nearly every category of sourcing, technology and managed services is rapidly reshaping legal and operational approaches to deal making. AI-driven technologies are a material component of nearly every services transaction today. This new paradigm has necessitated a rethinking of market norms and established approaches to key contractual terms, including IP ownership, service level agreements, performance warranties, indemnification provisions, and limitations on the parties’ liability to one another.

IP ownership

Under most legal systems, including US copyright law, a work must be authored by a human to qualify for copyright protection. When a provider uses generative AI to produce code, designs, reports or other deliverables, the protectability of those outputs and who owns them becomes legally uncertain. As a result, customers increasingly demand explicit contractual terms that address AI-generated outputs separately from human-authored work. These terms often take a “belt-and-suspenders” approach: a work-for-hire designation where possible, a broad assignment of rights (including a waiver of moral rights where permitted), and a fallback perpetual licence in case neither of those is effective.

Providers frequently rely on proprietary or third-party AI systems, including large language models and machine learning, to improve productivity and efficiency. This creates several negotiation pressure points. Providers typically argue that their models, training data, algorithms and fine-tuned configurations are pre-existing IP that must be excluded from any customer IP assignment. Customers, however, worry that deliverables are so intertwined with the AI tools that these carve-outs effectively strip them of meaningful ownership.

Many providers depend on third-party AI platforms whose terms of service may impose conditions on ownership or use of outputs. Customers increasingly require providers to disclose which platforms they use and to warrant that applicable third-party terms do not conflict with the customer’s IP rights.

In all cases, the rapid adoption of AI tools in sourcing arrangements has fundamentally complicated negotiations around the ownership of IP. Parties must now address new questions about authorship, protectability, training-data rights, patentability, third-party platform terms, and regulatory compliance. In this new environment, both customers and providers benefit from creating detailed, AI-specific IP ownership provisions rather than relying on legacy frameworks that never contemplated AI-generated works.

Service level agreements

Historically, service level agreements (SLAs) in sourcing contracts have been built around human-driven performance benchmarks, response times, resolution rates, processing volumes, and error rates calibrated to the capabilities of human teams. However, the rise of AI has introduced significant new considerations and challenges for both customers and providers. When a provider deploys AI-driven automation (eg, robotic process automation, AI-based customer service, or intelligent document processing), the parties must negotiate SLAs that address the unique characteristics of AI-delivered services. Negotiating these metrics is more complex than traditional SLAs because AI performance may make bright-line pass/fail thresholds more difficult to set. Where AI tools are used to deliver or monitor services, questions arise about liability for AI-driven failures, the adequacy of service credits as a remedy, and the circumstances under which AI-related performance failures should excuse or mitigate a provider’s responsibility for such failures.

Service providers are often hesitant to offer service levels for AI tools, citing difficulties around accuracy rates, error tolerances, bias monitoring, explainability requirements, and the inability to control inaccurate or harmful outputs. When an AI system fails or produces an incorrect output, determining fault is far more complex than with human error. AI tool performance is heavily dependent on the quality, completeness and timeliness of input data. Providers frequently seek to carve out SLA liability where poor performance is attributable to data supplied or controlled by a customer. Customers increasingly demand that SLA targets reflect AI-enhanced capabilities, faster response times, near-zero error rates, and continuous availability, since AI tools can operate continuously without fatigue. Accordingly, the integration of AI into sourcing contracts has led to a shift in negotiating SLA provisions, requiring tailored approaches to address the unique characteristics and risks of AI technology while balancing the potential benefits of automation and innovation.

Performance warranties

Traditionally, sourcing performance warranties have focused on human-delivered services and throughput tied to defined processes. As providers integrate AI tools to deliver or augment those services, the parties must reconsider how to define satisfactory performance. When an AI system produces inaccurate or substandard output, customers increasingly seek warranties that place responsibility on the provider regardless of whether the issue stems from the model, training data or human oversight. Providers, by contrast, often aim to limit liability for outcomes that are inherently probabilistic.

Customers may also demand transparency warranties requiring the provider to explain how an AI system generated a particular result, while providers resist such obligations, citing trade-secret concerns or technical limitations.

The widespread use of third-party AI models further complicates negotiations: customers want the provider to stand behind all outputs, whereas providers often seek carve-outs for failures attributable to AI platforms.

AI adoption has also created new categories of performance warranties absent from traditional sourcing. Customers increasingly require assurances that AI tools will not produce biased, discriminatory or otherwise unlawful outputs, particularly in HR, financial services and healthcare. Customers also seek enhanced warranties around system security and protection of input data, due to security risks introduced by AI systems, and they want assurances that AI-generated outputs do not infringe third-party IP rights, a contentious issue amidst ongoing generative-AI litigation.

Negotiating performance warranties has become much more complicated and the appropriate warranty terms for any sourcing arrangement will necessarily depend on the services involved, the AI tools used, the regulatory environment and each party’s risk tolerance.

Indemnification provisions

The rise of AI tools in sourcing arrangements has significantly reshaped how indemnity provisions are negotiated. Customers increasingly seek broader infringement indemnities that expressly cover claims arising from the provider’s use of AI tools, while providers push to limit liability for risks they view as inherent to AI systems and outside their control.

AI tools require access to substantial datasets, often including customer confidential information or personal data. This has intensified negotiations over data breach and privacy indemnities. Customers worry that inputting their data into third-party or cloud-based AI platforms could lead to unauthorised disclosure, data leakage, loss of trade-secret protections or regulatory violations. Customers extend indemnity clauses to cover losses stemming from AI-driven data processing, but providers seek to limit these obligations to instances of their own negligence or breach rather than accepting strict liability for failures of underlying AI platforms.

Where providers use AI to support professional or advisory services, such as legal research, financial analysis or technical recommendations, customers increasingly request indemnities for losses caused by inaccurate or “hallucinated” AI outputs. This differs from traditional sourcing indemnities, which contemplated human error rather than systemic algorithmic errors. Negotiations often focus on the provider’s duty to maintain human oversight and quality controls, with indemnity scope turning on whether the provider can demonstrate adequate review processes for AI-assisted work.

Many providers rely on third-party AI platforms rather than proprietary models and thus argue for a layered risk structure. Customers typically argue that providers should bear full responsibility for the tools they choose, including third-party AI, and should offer “flow-down” indemnities regardless of whether they can recover from the AI vendor. Providers contend that certain risks, such as fundamental flaws in widely used AI models, should be treated as force majeure or excluded from indemnity obligations altogether.

In sum, the integration of AI tools into sourcing arrangements has expanded both the scope and complexity of indemnity negotiations, driving the development of new indemnity categories and intensifying disputes over allocation of third-party risk.

Liability limits

The growing use of AI tools by service providers has reshaped negotiations around limitation-of-liability clauses. Historically, these provisions focused on service failures, data breaches and IP infringement. However, AI introduces a broader set of risks. When providers rely on AI to deliver services, such as analytics, automated decision-making or AI-assisted coding, the possibility of inaccurate, biased or irrational outputs creates new liability exposure. Customers often argue that these risks should fall outside standard caps or be treated as carve-outs. If AI produces discriminatory outcomes in areas such as HR outsourcing or claims processing, customers may face regulatory scrutiny and reputational harm, prompting demands for uncapped liability or higher super-caps.

AI-driven training on customer data adds further concerns around data protection, confidentiality and IP ownership, all of which influence liability discussions. Customers often contend that the potential scale of harm – regulatory fines, reputational damage and third-party claims – can far exceed contract value, increasing pressure on providers to accept higher caps for AI-related losses, especially where customers lack visibility into the AI systems used. Providers, in turn, argue that uncapped exposure is commercially unsustainable and seek to maintain aggregate caps while offering narrowly tailored, capped indemnities for defined AI failures.

Unfortunately, the market has not yet converged on a standard approach. Outcomes vary widely based on bargaining power, the criticality of the outsourced function, the nature of the AI tools involved, and the applicable regulatory environment.

Venture Capital

The rapid growth of the AI industry across the Boston innovation ecosystem has been fuelled, in large part, by the venture capital community. The technology companies developing foundational AI models – as well as the significant infrastructure, talent, data and energy demands required to support them – remain heavily dependent on venture financing to scale.

According to PitchBook’s 2025 NVCA Venture Monitor, at the end of 2025, approximately USD3.3 trillion in private company market value was tied to AI. AI-focused investments accounted for an estimated 65.4% of total venture deal value in 2025, representing roughly 39.4% of completed transactions. Boston-area companies continue to play a leading role in this growth, particularly at the intersection of AI, life sciences, healthcare and enterprise software.

Private Equity and M&A Activity

AI is rapidly transforming private equity transactions across the entire deal life cycle, from sourcing and diligence through exit. While adoption levels vary across the industry, one principle is increasingly clear: when implemented effectively, AI creates a meaningful competitive advantage in processes where speed, precision and data analysis are critical. This is equally true for funds and law firms helping to execute on deals.

At its core, AI is another tool in the efficiency toolbox. AI-powered tools enable private equity firms and their advisers, including legal, financial and accounting professionals, to process significantly larger volumes of structured and unstructured data in far less time. These tools can extract insights from extensive document sets at a scale that would be impractical through traditional methods.

One of the most immediate effects of AI adoption is the compression of transaction timelines. Tasks such as financial and legal diligence that traditionally required weeks of manual work can now be completed in a fraction of the time. While human judgement remains essential to ensure the integrity of the dataset and resulting analysis, AI significantly reduces the time required to conduct that analysis. This has important implications for competitive processes. In auction settings, where speed can determine access and success, buyers must now be able to evaluate opportunities and respond with high-quality bid materials on accelerated timelines. Firms that cannot keep pace risk losing competitive positioning early in the process.

At the same time, this compression of timelines can benefit smaller or more targeted investment platforms. Because AI reduces reliance on large teams for data processing, firms that were previously disadvantaged by slower manual workflows can now respond more quickly and compete effectively in fast-moving processes. These tools can enable deal teams to spend less time on data collection and processing and more time on higher-value strategic analysis.

This shift is particularly impactful for mid-sized and boutique private equity firms and their advisers. Historically, larger firms held a distinct advantage in transaction execution by deploying larger teams to manage labour-intensive processes such as diligence, modelling and analysis. By automating data-intensive tasks, AI allows smaller, highly sophisticated teams to achieve levels of output previously reserved for organisations with significantly greater headcount. Lower-leverage teams can compete on more equal footing with larger firms in data-driven aspects of transactions.

Ultimately, for smaller and mid-sized private equity firms, the rise of AI is an encouraging development. As these technologies become more accessible and easier to implement, they reduce many of the historical advantages associated with sheer size and manpower. Firms that are agile, focused and thoughtful in adopting AI tools are well positioned to narrow the gap with larger competitors and, in some cases, outperform them.

Conclusion

Boston stands at the forefront of AI-driven innovation. As regulatory regimes continue to develop and market practices mature, organisations operating in this space must remain agile – continuously reassessing their compliance frameworks, contractual structures and investment strategies. Those that align legal risk management with technological advancement will be best positioned to capitalise on AI’s transformative potential and navigate its inherent uncertainties.